NPS students build NATO tool to track battlefield emissions

By AI, Created 11:11 AM UTC, May 20, 2026, /AGP/ – Naval Postgraduate School students developed a machine learning tool called FASTCAT to help NATO analysts find and track adversary electromagnetic emissions. The two-month project was briefed to NATO leaders in December 2025 and later to NATO’s JATEC in Poland, where it reportedly outperformed a separately funded effort.

Why it matters: - NATO and U.S. operators need faster ways to sort battlefield electromagnetic emissions and identify where adversary assets may be hiding. - FASTCAT is designed to turn sparse emissions data into usable intelligence for targeting, surveillance and reconnaissance. - The project shows how military graduate students can build mission tools quickly and at low cost.

What happened: - Naval Postgraduate School students built FASTCAT, a machine learning tool that clusters and maps battlefield emissions data. - NATO supplied synthetic battlefield emission data from Ukraine to test the concept. - The project was completed in about two months as part of the students’ coursework. - NPS briefed the tool to French Navy Adm. Pierre Vandier, NATO Supreme Allied Commander for Transformation, in Norfolk in December 2025. - NPS later briefed NATO’s Joint Analysis, Training, and Education Centre in Bydgoszcz, Poland.

The details: - NATO gave the NPS team 34,000 synthetic electromagnetic emission data points covering a three-month period. - Each data point included frequency, timestamp, and possible latitude and longitude. - The dataset did not include signal strength, duration, collection method, source identity, or source movement. - FASTCAT stands for Frequency-based Algorithm for Spatial and Temporal Clustering Analysis with Thresholds. - The tool searches for spatial, temporal and frequency relationships among emissions. - FASTCAT uses open-source resources to add geographic context such as forest, urban or water terrain. - Users can adjust four parameters: distance between points, number of nearby points, time, and emission frequency. - The interface presents clusters, networks and geographic layers on a map with a dashboard for adding and removing layers. - FASTCAT was coded in Python and runs on a laptop without cloud access or high-performance computing. - The system is built for rapid ingestion of newer operational data with more detail than the synthetic test set. - The NPS team included U.S. Navy Lt. Elliot Kim, Lt. j.g. Taylor Haist, U.S. Army Maj. Cody Ward and Royal Australian Air Force Squadron Leader Darby Nelson. - The students were in the quarter-long course “Case Studies in Applied Defense Analytics” and completed the project as a capstone separate from thesis research. - U.S. Army Lt. Col. Rob Froberg co-instructed the course and mentored the team. - David Alderson, DSAG executive director and operations research department chair, said NPS students can build products that compete with industry tools. - Alderson said the FASTCAT briefing led to ongoing NATO-sponsored work for DSAG. - U.S. Navy Vice Adm. Jeffrey W. Hughes said NPS can help turn Ukraine combat data into interoperable capabilities for U.S. and allied warfighters. - U.S. Army Lt. Col. Devin Eselius represented NATO on the project and said the team integrated the interface into a Ukrainian battlefield system. - Eselius said the project showed the impact a small group of coding-competent military operators can have when they have access to data, tools and operational problems. - Kim said NPS master’s students can help fill analytical gaps across the Defense Department before they return to the fleet.

Between the lines: - The core advantage is not just the algorithm. It is the combination of operational experience, machine learning and interface design. - FASTCAT is built for imperfect data, which is common in wartime collection. - The project also reflects NATO’s push to turn lessons from Ukraine into usable battlefield methods faster. - The fact that the tool was developed inside an academic setting suggests a low-friction path for defense partners seeking rapid prototypes. - The report’s comparison to a parallel NATO-funded effort points to speed and specialization as major differentiators, though the release does not provide technical benchmarks.

What’s next: - NATO-sponsored work will continue through DSAG. - FASTCAT is positioned for use with live operational data as additional information becomes available. - Future development may expand the tool’s predictive capabilities, which Kim said he explored during the project. - The partnership between NPS and NATO appears likely to continue as both sides look for faster ways to convert battlefield data into action.